Object detection is a long-standing topic in computer vision.
Recently, a new family of object detectors, named detection transformers, has drawn increasing attention due to their simplicity and promising performance. The pioneer work of this class of methods is DETR [1]}, which views object detection as a direct set prediction problem and applies a transformer to translate the object queries to the target objects. It achieves better performance than the seminal Faster RCNN [2]} on the commonly used COCO dataset [3]}, but its convergence is significantly slower than that of CNN-based detectors. For this reason, most of the subsequent works have been devoted to improving the convergence of DETR, through efficient attention mechanism [4]}, conditional spatial query [5]}, regression-aware co-attention [6]}, etc. These methods are able to achieve better performance than Faster RCNN with comparable training costs on the COCO dataset, demonstrating the superiority of detection transformers.
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